{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\Anaconda3\\lib\\importlib\\_bootstrap.py:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 192 from C header, got 216 from PyObject\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(400, 11)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Cards</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Student</th>\n",
       "      <th>Married</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.891</td>\n",
       "      <td>3606</td>\n",
       "      <td>283</td>\n",
       "      <td>2</td>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106.025</td>\n",
       "      <td>6645</td>\n",
       "      <td>483</td>\n",
       "      <td>3</td>\n",
       "      <td>82</td>\n",
       "      <td>15</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Asian</td>\n",
       "      <td>903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104.593</td>\n",
       "      <td>7075</td>\n",
       "      <td>514</td>\n",
       "      <td>4</td>\n",
       "      <td>71</td>\n",
       "      <td>11</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Asian</td>\n",
       "      <td>580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>148.924</td>\n",
       "      <td>9504</td>\n",
       "      <td>681</td>\n",
       "      <td>3</td>\n",
       "      <td>36</td>\n",
       "      <td>11</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Asian</td>\n",
       "      <td>964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>55.882</td>\n",
       "      <td>4897</td>\n",
       "      <td>357</td>\n",
       "      <td>2</td>\n",
       "      <td>68</td>\n",
       "      <td>16</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Caucasian</td>\n",
       "      <td>331</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Income  Limit  Rating  Cards  Age  Education  Gender Student Married  \\\n",
       "1   14.891   3606     283      2   34         11    Male      No     Yes   \n",
       "2  106.025   6645     483      3   82         15  Female     Yes     Yes   \n",
       "3  104.593   7075     514      4   71         11    Male      No      No   \n",
       "4  148.924   9504     681      3   36         11  Female      No      No   \n",
       "5   55.882   4897     357      2   68         16    Male      No     Yes   \n",
       "\n",
       "   Ethnicity  Balance  \n",
       "1  Caucasian      333  \n",
       "2      Asian      903  \n",
       "3      Asian      580  \n",
       "4      Asian      964  \n",
       "5  Caucasian      331  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r'E:\\programming\\dataset\\Into_to_statstical_learning\\Credit.csv',index_col=0)\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data[['Income','Limit','Rating','Student']]\n",
    "y = data['Balance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Income</th>\n",
       "      <th>Limit</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Student</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14.891</td>\n",
       "      <td>3606</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106.025</td>\n",
       "      <td>6645</td>\n",
       "      <td>483</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>104.593</td>\n",
       "      <td>7075</td>\n",
       "      <td>514</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>148.924</td>\n",
       "      <td>9504</td>\n",
       "      <td>681</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>55.882</td>\n",
       "      <td>4897</td>\n",
       "      <td>357</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    Income  Limit  Rating  Student\n",
       "1   14.891   3606     283        0\n",
       "2  106.025   6645     483        1\n",
       "3  104.593   7075     514        0\n",
       "4  148.924   9504     681        0\n",
       "5   55.882   4897     357        0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['Student'] = X['Student'].map({'Yes':1,'No':0})\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept is  520.0150000000001\n",
      "Coef are  [-279.65262476  280.51607343  338.48984662  126.80051361]\n"
     ]
    }
   ],
   "source": [
    "# Linear regression (least square fit)\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_scaled,y)\n",
    "print('Intercept is ',lr.intercept_)\n",
    "print('Coef are ',lr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept is  520.0150000000001\n",
      "Coef are  [-276.1610234   291.29512888  324.17011678  126.4516386 ]\n"
     ]
    }
   ],
   "source": [
    "# Ridge regression \n",
    "ridge = Ridge(alpha = 1)\n",
    "ridge.fit(X_scaled,y)\n",
    "print('Intercept is ',ridge.intercept_)\n",
    "print('Coef are ',ridge.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can see for alpha = 1, we are having almost same value, there is very less effect of regularization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept is  520.0150000000001\n",
      "Coef are  [-247.2563987   289.09143853  296.87414298  123.05532307]\n"
     ]
    }
   ],
   "source": [
    "#increasing the values of alpha\n",
    "ridge = Ridge(alpha = 10)\n",
    "ridge.fit(X_scaled,y)\n",
    "print('Intercept is ',ridge.intercept_)\n",
    "print('Coef are ',ridge.coef_)\n",
    "# see the values decreasing, and moving towards zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept is  520.015\n",
      "Coef are  [0.83828411 1.56753041 1.57106622 0.47383595]\n"
     ]
    }
   ],
   "source": [
    "# Large value of alpha\n",
    "ridge = Ridge(alpha = 100000)\n",
    "ridge.fit(X_scaled,y)\n",
    "print('Intercept is ',ridge.intercept_)\n",
    "print('Coef are ',ridge.coef_)\n",
    "# all the values are pretty much close to 0 now. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://seaborn.pydata.org/generated/seaborn.lineplot.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = [1e-1,1e1,1e2,1e5,1e7,1e9]\n",
    "intercept_dict = {}\n",
    "\n",
    "for a in alpha:\n",
    "    ridge = Ridge(alpha = a)\n",
    "    ridge.fit(X_scaled,y)\n",
    "    intercept_dict[a] = list(ridge.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1.000000e-01</th>\n",
       "      <td>-279.302308</td>\n",
       "      <td>282.432925</td>\n",
       "      <td>336.217625</td>\n",
       "      <td>126.768867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.000000e+01</th>\n",
       "      <td>-247.256399</td>\n",
       "      <td>289.091439</td>\n",
       "      <td>296.874143</td>\n",
       "      <td>123.055323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.000000e+02</th>\n",
       "      <td>-98.926545</td>\n",
       "      <td>210.494675</td>\n",
       "      <td>212.169527</td>\n",
       "      <td>98.060125</td>\n",
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       "    <tr>\n",
       "      <th>1.000000e+05</th>\n",
       "      <td>0.838284</td>\n",
       "      <td>1.567530</td>\n",
       "      <td>1.571066</td>\n",
       "      <td>0.473836</td>\n",
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       "    <tr>\n",
       "      <th>1.000000e+07</th>\n",
       "      <td>0.008515</td>\n",
       "      <td>0.015826</td>\n",
       "      <td>0.015861</td>\n",
       "      <td>0.004757</td>\n",
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       "    <tr>\n",
       "      <th>1.000000e+09</th>\n",
       "      <td>0.000085</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>0.000159</td>\n",
       "      <td>0.000048</td>\n",
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      ],
      "text/plain": [
       "                       0           1           2           3\n",
       "1.000000e-01 -279.302308  282.432925  336.217625  126.768867\n",
       "1.000000e+01 -247.256399  289.091439  296.874143  123.055323\n",
       "1.000000e+02  -98.926545  210.494675  212.169527   98.060125\n",
       "1.000000e+05    0.838284    1.567530    1.571066    0.473836\n",
       "1.000000e+07    0.008515    0.015826    0.015861    0.004757\n",
       "1.000000e+09    0.000085    0.000158    0.000159    0.000048"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp = pd.DataFrame(intercept_dict).T\n",
    "tmp.index = alpha\n",
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Alpha')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16,8))\n",
    "sns.lineplot(data = tmp)\n",
    "plt.axhline(y = 0,linestyle = 'dashed',lw = 0.1,color = 'black')\n",
    "plt.xticks(alpha)\n",
    "plt.xlim(0,10)\n",
    "plt.ylabel('Coeffients')\n",
    "plt.xlabel('Alpha')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Alpha')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using log scale for x axis\n",
    "\n",
    "plt.figure(figsize = (16,8))\n",
    "sns.lineplot(data = tmp)\n",
    "plt.axhline(y = 0,linestyle = 'dashed',lw = 0.1,color = 'black')\n",
    "plt.xticks(alpha)\n",
    "plt.xscale('log')\n",
    "plt.ylabel('Coeffients')\n",
    "plt.xlabel('Alpha')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
